We present, in Fig. REF , a comparison between the image regions that were responsible for CNNs prediction (i.e., activation maps) of SegNet [1]} and UNet [2]} models with and without MTL.
We notice that the activation maps are better adjusted to the objects' contours (white bounding box).
This behavior happens due to the better distributed latent space (i.e., decoder stage) on the models trained with MTL, see Fig. REF .
The latent space's clustering behavior provides the networks' ability to use regions they did not use before to make the segmentation prediction.
